Obstacle mapping is crucial to the robust operation of robotic networks. Specifically, obstacle mapping relates to determining the location of an object within an environment. In existing approaches, only the obstacles that are directly visible to the robot can be mapped. In other words, occluded obstacles cannot be mapped. Currently, frameworks for mapping of occluded obstacles are lacking.
Autonomous vehicles are widely used and include a variety of unmanned ground vehicles, underwater vehicles, and aerospace vehicles, such as robots and unmanned aerial vehicles (“UAVs”). Autonomous vehicles make decisions and respond to situations completely without human intervention. There are major limitations to the overall performance, accuracy, and robustness of navigation and control of an autonomous vehicle. In order to perform navigation properly, an autonomous vehicle must be able to sense its location, steer toward a desired destination, and avoid obstacles.
Various modalities have been used to provide navigation of autonomous vehicles. These include use of the Global Positioning System (“GPS”), inertial measurements from sensors, and image measurements from cameras. Smaller UAVs are being developed for reconnaissance and surveillance that can be carried and deployed in the field by an individual or a small group. Such UAVs include micro air vehicles (“MAVs”) and organic air vehicles (“OAVs”), which can be remotely controlled. Such air vehicles can be designed for operation in a battlefield by troops, and provide small combat teams and individual soldiers with the capability to detect enemy forces concealed in forests or hills, around buildings in urban areas, or in places where there is no direct line-of-sight. Some of these air vehicles can perch and stare, and essentially become sentinels for maneuvering troops.
In order to avoid obstacles during navigation, autonomous vehicles such as UAVs need obstacle mapping. Typical vehicle sensors currently used (e.g., scanning laser detection and ranging (“LADAR”) can not map occluded obstacles. An important issue key to the robust operation of a mobile robotic network is the accurate mapping of the obstacles. Accurate mapping of obstacles is challenging in that the high volume of information presented by the environment makes it prohibitive to sense all areas. Typically, mobile robotic networks only map areas that are directly sensed such as through use of Simultaneous Localization and Mapping (“SLAM”). SLAM approaches mainly focus on reducing the uncertainty in the sensed obstacles by using a Kalman filter. Similarly, approaches based on generating an occupancy map also address sensing uncertainty. Another set of approaches are based on the Next Best View (“NBV”) approach. In NBV approaches, the aim is to move to the positions that are “good” for sensing, by guiding the mobile unit to the perceived next safest area or area with the most visibility based on the current map. However, areas that are not sensed directly are not mapped in NBV.
When using conventional detection units to generate a map of an environment, the map plots where the obstacles are and where there are no obstacles in the environment. However, conventional detection units cannot access every location of the environment as obstacles frequently block one or more paths of the detection units. Furthermore, obstacles can be dynamic resulting in obstacles which were previously un-obscured, being partially or completely obscured. Thus, there is a need for a see-through based mapping of obstacles. The present invention satisfies this need.